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Chronic Illness and Impoverishment in Urban Bangladesh: Evidence from Household Income and Expenditure Survey 2022

Using 2022 Household Income and Expenditure Survey data, this study reveals that chronic illness and Out-of-Pocket healthcare spending in urban Bangladesh create a vicious cycle where households are significantly more likely to fall into poverty due to medical costs, while poverty simultaneously increases the risk of chronic illness.

Original authors: Azima Begum, Muhammad Shahadat Hossain Siddiquee

Published 2026-07-14

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Azima Begum, Muhammad Shahadat Hossain Siddiquee

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine your family's monthly budget is a giant, colorful pie. You've sliced it up carefully: a big chunk for food, a slice for rent, a piece for school supplies, and a tiny, precious crumb for emergencies. Now, imagine a sudden, invisible storm hits your house. This storm isn't made of rain, but of chronic illnesses—long-term health problems like high blood pressure, diabetes, or stomach ulcers that don't just go away after a week.

In the bustling cities of Bangladesh, this storm is hitting hard. A new study, using a massive snapshot of 7,133 city households taken in 2022, reveals exactly how this storm turns a family's financial pie into a disaster.

The "Out-of-Pocket" Leak

Here's the tricky part: in Bangladesh, when you get sick, you usually pay for it yourself right then and there. This is called Out-of-Pocket (OOP) spending. It's like having a hole in your pocket where your money leaks out before you even get home.

The study found that nearly 74% of all healthcare costs in the country come from this leaky pocket. For families with chronic illnesses, this leak is a firehose. While a healthy family might spend about 3.5% of their total budget on health, a family fighting a chronic disease spends a whopping 7.5%. That's more than double the drain on their resources!

The Poverty Trap: A Two-Way Street

The most fascinating discovery is that this isn't just a one-way street where sickness makes you poor. It's a vicious circle, a two-way trap.

Sickness pushes you down: The study shows that if you have a chronic illness, you are 3.5 percentage points more likely to fall into poverty compared to a healthy family. In fact, about 2.77% of all urban households in Bangladesh were pushed below the poverty line solely because they had to pay for their medical bills. That's like 4.6 million people suddenly finding their feet underwater because they had to buy medicine.
Poverty makes you sicker: But the trap works the other way, too. The researchers found that families who are already poor are 24.2 percentage points more likely to have a chronic illness. Being poor makes it harder to stay healthy, which leads to more sickness, which leads to being even poorer. It's a spiral where the bottom keeps getting deeper.

The "Pen's Parade" of Falling

To visualize this, the authors used a cool graph called a "Pen's Parade." Imagine a line of people ranked from the richest to the poorest, holding hands. There's a red line drawn across the middle representing the "poverty line."

Before paying for medicine, many families are safely above that red line. But as they pay their medical bills, the line of people drops. The study found that for families with chronic illnesses, this drop is massive. Their "poverty gap" (how far below the line they fall) increases by BDT 164 on average, which is nearly double the drop for healthy families. It's like a heavy anchor suddenly attached to their ankles, dragging them down faster than anyone else.

Who Gets Hit Hardest?

The storm doesn't hit everyone equally.

The Rich vs. The Poor: The poorest families (the 1st income quintile) are hit hardest. About 36% of them face "Catastrophic Health Expenditure" (spending more than 10% of their total budget on health), compared to only 24% of the richest families.
The City vs. The Country: While rural areas suffer too, the urban poor are in a unique bind. The study notes that 71.37% of urban households have at least one member with a chronic illness.
The Numbers: In the city, the average monthly health cost for a family with a chronic illness is BDT 2,980, compared to BDT 2,429 in rural areas.

What the Study Doesn't Say

It's important to know what this study doesn't claim. The authors are very clear that they cannot prove that one specific thing causes the other in a strict, scientific experiment sense because they looked at a single snapshot in time (2022). They can't say, "If you eat this specific food, you will definitely get sick," because they didn't track people's diets or habits like smoking or exercise. They also didn't test new medicines or insurance plans; they just measured the current situation.

The Takeaway

The paper suggests that the current system is like a leaky boat. When the water (medical bills) comes in, the families with chronic illnesses are the ones taking on water the fastest, sinking below the surface of poverty.

The authors argue that to fix this, we need a better "life raft." They suggest that risk pooling—like health insurance where everyone chips in a little so no single family has to pay the whole bill alone—could stop the leak. They also point out that making healthcare affordable and accessible for the urban poor is crucial to breaking the cycle where poverty makes you sick, and sickness keeps you poor.

In short, chronic illness in urban Bangladesh isn't just a medical problem; it's a financial earthquake that shakes families out of their homes and into poverty, and the only way to stop the shaking is to build a stronger foundation for everyone.

Technical Summary: Chronic Illness and Impoverishment in Urban Bangladesh

Problem Statement
Chronic illnesses are a leading cause of global mortality and a primary driver of household impoverishment, particularly in low- and middle-income countries where Out-Of-Pocket (OOP) healthcare expenditures are high. In Bangladesh, OOP payments constitute nearly 74% of total healthcare costs, creating a significant financial burden that pushes households into poverty. While existing literature addresses the economic impact of OOP spending and the prevalence of Non-Communicable Diseases (NCDs) separately, there is a scarcity of evidence regarding the specific interlinkages between chronic illness and urban impoverishment in Bangladesh. This study addresses this gap by investigating how chronic illness drives households into poverty through OOP expenditures and conversely, how poverty exacerbates the risk of chronic illness, specifically within the urban context using the latest nationally representative data.

Methodology
The study utilizes data from the Household Income and Expenditure Survey (HIES) 2022, conducted by the Bangladesh Bureau of Statistics (BBS). The analytical sample consists of 7,133 urban households drawn from a final dataset of 14,269 households.

Poverty Measurement: The Cost of Basic Needs (CBN) approach is employed to estimate the poverty line, set at BDT 3,832 per capita monthly expenditure. Impoverishment is measured as the difference between pre-payment and post-payment poverty indicators. Specifically, the study calculates the change in poverty headcount ( $H_{pre}$ vs. $H_{post}$ ), poverty gap ( $G_{pre}$ vs. $G_{post}$ ), and normalized poverty gap to quantify the impoverishing effect of OOP payments.
Catastrophic Health Expenditure (CHE): CHE is defined using two thresholds: OOP exceeding 10% of total household consumption expenditure and OOP exceeding 40% of non-food expenditure (capacity to pay).
Statistical Analysis:
- Descriptive Statistics: Bivariate analyses (Chi-square and t-tests) examine associations between chronic illness status and demographic/socioeconomic variables.
- Probit Regression: Two distinct Probit models are estimated to establish bidirectional interlinkages:
  1. Model 1: Examines the probability of a household becoming impoverished due to OOP payments, with chronic illness as the core explanatory variable.
  2. Model 2: Examines the probability of a household suffering from chronic illness, with impoverishment status as the core explanatory variable.
- Visualization: Pen's Parade graphs are constructed to visualize the shift in household expenditure distributions relative to the poverty line before and after OOP payments.

Key Results

Prevalence: Approximately 29.17% of urban individuals and 71.37% of urban households report at least one chronic illness. The most prevalent conditions are gastric/ulcer (8.28%), high blood pressure (8.01%), and diabetes (5.06%).
OOP Expenditure: Households with chronic illness incur significantly higher OOP costs (BDT 2,980 monthly) compared to those without (BDT 1,002). OOP spending accounts for 7.5% of total expenditure for households with chronic illness, compared to 3.5% for those without.
Impoverishment Impact:
- Overall, 2.77% of urban households fall into poverty due to OOP payments.
- Households with chronic illness face a substantially higher impoverishment rate (3.40%) compared to those without (1.22%).
- The normalized poverty gap for households with chronic illness is 4.29%, nearly double that of households without chronic illness (2.31%).
Regression Findings:
- Chronic Illness $\rightarrow$ Impoverishment: Households with at least one chronic illness are 3.5 percentage points more likely to be impoverished due to OOP payments ( $p<0.01$ ) compared to those without. The risk increases with the number of chronic conditions; households with more than two chronic illnesses show a 6.1 percentage point higher probability of impoverishment.
- Impoverishment $\rightarrow$ Chronic Illness: Impoverished households are 24.2 percentage points more likely to have a chronic illness ( $p<0.01$ ), indicating that poverty significantly enhances the risk of developing chronic diseases.
Regional Variation: Impoverishment due to OOP varies by division, with Sylhet showing the highest increase (4.00 percentage points) and Rangpur the lowest (2.01 percentage points), though Rangpur has the highest overall poverty rate.

Key Contributions

Novel Evidence: This study provides the first evidence using the HIES 2022 dataset to explicitly map the bidirectional interlinkages between chronic illness and urban impoverishment in Bangladesh.
Urban Focus: Unlike previous studies that often aggregate rural and urban data or focus solely on rural settings, this research isolates the urban context, where poverty reduction rates have slowed and the burden of NCDs is rising.
Quantification of Impact: The study quantifies the specific impoverishment effect of chronic illness, demonstrating that the financial burden is nearly three times higher for affected households compared to unaffected ones.
Policy-Relevant Metrics: By utilizing Pen's Parade graphs and calculating normalized poverty gaps, the study offers a granular view of how health expenditures deepen poverty intensity, not just headcount.

Significance and Claims
The paper claims that chronic illness and urban impoverishment in Bangladesh are mutually reinforcing. The findings suggest that OOP healthcare costs driven by chronic disease are a critical mechanism pushing non-poor urban households into poverty. Conversely, the financial constraints of poverty limit access to preventive care and healthy living conditions, thereby increasing the risk of chronic disease.

The authors conclude that addressing this cycle requires:

Risk Pooling Mechanisms: Implementing effective pre-payment systems, such as social health insurance or community-based schemes, to reduce the direct financial burden on households.
Targeted Urban Health Protection: Developing accessible and affordable urban health protection schemes specifically for low-income populations suffering from chronic illnesses, in alignment with the Bangladesh National Urban Health Strategy.
Prevention and Control: Prioritizing the detection, screening, and management of chronic diseases to prevent the financial shock that leads to impoverishment.

The study acknowledges limitations, including its reliance on secondary cross-sectional data which precludes definitive causal inference, and the exclusion of specific behavioral risk factors (e.g., obesity, diet) in the regression models. However, it asserts that the evidence presented is sufficient to warrant urgent policy intervention to break the cycle of illness and poverty in urban Bangladesh.